Various embodiments are provided for intelligent application of operational rules to operational data in a computing environment by a processor. One or more operational rules may be extracted and formalized from a knowledge graph, a domain knowledge, or a combination thereof describing one or more operational policies and conditions. The one or more operational rules may be applied to operational data to identify and filter non-compliant operational data.
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2. The method of claim 1, further including assigning a score to the one or more operational rules indicating a probability of compliance or non-compliance for the operational data.
This invention relates to systems for monitoring and evaluating operational data against predefined rules to assess compliance. The technology addresses the challenge of efficiently determining whether operational data meets specified criteria, which is critical in industries like finance, healthcare, and manufacturing where regulatory compliance is mandatory. The method involves analyzing operational data to detect deviations from one or more operational rules. These rules define acceptable parameters or behaviors for the data. The system identifies instances where the data does not comply with these rules, flagging potential issues for further review. Additionally, the method assigns a score to each operational rule, representing the likelihood that the data will either comply or fail to comply with the rule. This scoring mechanism helps prioritize compliance risks, allowing organizations to focus resources on areas with the highest probability of non-compliance. The scoring may be based on historical data, statistical models, or real-time analysis, providing a dynamic assessment of compliance risk. By integrating rule-based evaluation with probabilistic scoring, the system enhances decision-making and regulatory adherence in automated operational monitoring.
3. The method of claim 1, further including creating the one or more non-compliant operational rules from the one or more operational rules based on user feedback, operational acceptability criteria, historical data, or a combination thereof.
This invention relates to a system for generating and refining operational rules in a technical or industrial environment. The method involves creating operational rules that define acceptable performance parameters for a system or process. These rules are used to monitor and control operations, ensuring they remain within specified limits. The invention further includes a process for identifying and creating non-compliant operational rules, which are rules that deviate from acceptable standards. These non-compliant rules are derived from user feedback, operational acceptability criteria, historical data, or a combination of these sources. By analyzing user feedback, the system can identify rules that may not align with practical operational needs. Operational acceptability criteria provide predefined thresholds for what constitutes a compliant rule. Historical data allows the system to detect patterns or anomalies that indicate non-compliance. The combination of these inputs ensures that the rules are continuously refined to improve system performance and reliability. This approach helps maintain operational efficiency while adapting to changing conditions or requirements.
4. The method of claim 1, further including learning those of the one or more policies or conditions from the knowledge graph that identify the operational data as being noncompliant operational data from historical data, user feedback, one or more non-compliant operational rules, or a combination thereof.
This invention relates to a system for identifying noncompliant operational data using a knowledge graph. The system addresses the challenge of detecting deviations from expected operational behavior in large-scale data environments, where manual review is impractical. The knowledge graph stores policies, conditions, and operational rules that define compliance requirements. The system analyzes operational data against these rules to determine compliance. The method includes learning which policies or conditions in the knowledge graph flag data as noncompliant. This learning process uses historical data, user feedback, predefined non-compliant rules, or a combination of these sources. By continuously updating the knowledge graph with new compliance insights, the system improves its ability to accurately identify noncompliant data over time. The approach reduces reliance on static rule sets, enabling adaptive compliance monitoring in dynamic environments. The system may also integrate feedback loops to refine detection accuracy based on user corrections or additional rule inputs. This method enhances operational efficiency by automating compliance checks and reducing false positives or negatives in data analysis.
8. The system of claim 7, wherein the executable instructions further assign a score to the one or more operational rules indicating a probability of compliance or noncompliance for the operational data.
The system is designed for monitoring and analyzing operational data to ensure compliance with predefined operational rules. The system includes a processor and a memory storing executable instructions. These instructions enable the system to receive operational data from one or more sources, such as sensors or databases, and compare this data against a set of operational rules. The rules define acceptable or unacceptable conditions for the operational data. The system evaluates the operational data to determine whether it meets the criteria specified in the rules. Additionally, the system assigns a score to each operational rule, where the score represents the probability that the operational data either complies with or violates the rule. This scoring mechanism helps prioritize compliance issues and identify areas where noncompliance is more likely. The system may also generate alerts or reports based on the evaluation results, allowing for timely corrective actions. The overall goal is to enhance operational efficiency and regulatory adherence by continuously monitoring and assessing data against established rules.
9. The system of claim 7, wherein the executable instructions further create the one or more non-compliant operational rules from the one or more operational rules based on user feedback, operational acceptability criteria, historical data, or a combination thereof.
This invention relates to a system for generating and managing operational rules in a technical or industrial environment, particularly focusing on identifying and addressing non-compliant rules. The system includes a processor and memory storing executable instructions that create operational rules for controlling processes or systems. A key feature is the ability to generate non-compliant operational rules by analyzing user feedback, operational acceptability criteria, historical data, or a combination of these factors. The system evaluates existing operational rules to determine which ones do not meet specified standards or requirements, then flags or modifies them as non-compliant. This helps ensure that only compliant rules are applied, improving system reliability and performance. The system may also include a user interface for inputting feedback or criteria, and a database for storing historical data. By dynamically adjusting rules based on real-world performance and user input, the system enhances adaptability and compliance in automated or semi-automated processes. The invention is particularly useful in industries where strict adherence to operational standards is critical, such as manufacturing, energy, or transportation.
10. The system of claim 7, wherein the executable instructions further learn those of the one or more policies or conditions from the knowledge graph that identify the operational data as being non-compliant operational data from historical data, user feedback, one or more non-compliant operational rules, or a combination thereof.
This invention relates to a system for identifying non-compliant operational data using a knowledge graph. The system addresses the challenge of detecting deviations from compliance policies in operational data, which is critical for regulatory adherence and operational integrity in industries like finance, healthcare, and manufacturing. The system includes a knowledge graph that stores policies, conditions, and operational rules. Executable instructions within the system analyze operational data against these rules to determine compliance. The system further enhances its detection capabilities by learning from multiple sources, including historical data, user feedback, and predefined non-compliant operational rules. By integrating these inputs, the system dynamically updates its understanding of what constitutes non-compliant data, improving accuracy over time. The knowledge graph serves as a centralized repository, enabling the system to correlate different compliance criteria and operational contexts. The learning mechanism allows the system to adapt to evolving compliance requirements and emerging patterns of non-compliance. This adaptive approach ensures that the system remains effective even as operational environments and regulatory standards change. The system's ability to incorporate user feedback further refines its detection accuracy, making it more responsive to real-world operational challenges.
14. The computer program product of claim 13, further including an executable portion that assigns a score to the one or more operational rules indicating a probability of compliance or non-compliance for the operational data.
This invention relates to a computer program product for evaluating operational data against predefined rules to determine compliance. The system processes operational data from a computing environment, compares it against one or more operational rules, and generates an output indicating whether the data complies with the rules. The rules may be defined by a user or administrator and can include conditions, thresholds, or other criteria relevant to the operational data. The system may also analyze the operational data to identify patterns, anomalies, or deviations from expected behavior. Additionally, the system assigns a score to each operational rule, representing the probability that the operational data will comply or fail to comply with the rule. This scoring mechanism helps prioritize rules based on their likelihood of being violated, allowing for more efficient monitoring and compliance management. The system may also include a user interface for displaying the compliance results, rule scores, and other relevant information to users or administrators. The invention is particularly useful in environments where regulatory compliance, security policies, or operational standards must be enforced and monitored.
15. The computer program product of claim 13, further including an executable portion that creates the one or more non-compliant operational rules from the one or more operational rules based on user feedback, operational acceptability criteria, historical data, or a combination thereof.
This invention relates to a computer program product for managing operational rules in a system, particularly focusing on identifying and addressing non-compliant rules. The system monitors operational rules to detect deviations from compliance standards, which may arise due to user feedback, operational acceptability criteria, or historical data. When a rule is flagged as non-compliant, the system generates one or more non-compliant operational rules that reflect the identified issues. These non-compliant rules are then used to adjust the system's behavior, ensuring that future operations adhere to the required standards. The system may also analyze historical data to predict potential compliance issues before they occur, allowing for proactive adjustments. By integrating user feedback, the system can refine its rules based on real-world usage patterns, improving overall system performance and reliability. The invention aims to enhance compliance monitoring and rule management in automated systems, reducing errors and ensuring consistent operational standards.
16. The computer program product of claim 13, further including an executable portion that learns those of the one or more policies or conditions from the knowledge graph that identify the operational data as being non-compliant operational data from historical data, user feedback, one or more non-compliant operational rules, or a combination thereof.
This invention relates to a computer program product for analyzing operational data using a knowledge graph to identify non-compliant data. The system leverages a knowledge graph to store and process policies, conditions, and operational rules that define compliance requirements. The executable portion of the program learns which policies or conditions flag operational data as non-compliant by analyzing historical data, user feedback, and predefined non-compliant rules. The knowledge graph dynamically updates based on these inputs, improving accuracy over time. The system may also include a compliance engine that evaluates operational data against the learned policies and conditions to detect deviations. The learned insights can be used to refine compliance rules, automate corrective actions, or generate alerts for further review. The invention aims to enhance compliance monitoring by combining rule-based and machine learning approaches, reducing manual intervention and improving detection of non-compliant operations.
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May 8, 2019
April 23, 2024
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